Multisensor-Based 3D Gesture Recognition for a Decision-Making Training System

Pan, Tse-Yu; Chang, Chen-Yuan; Tsai, Wen-Hsuan; Hu, Min‐Chun

doi:10.1109/jsen.2020.3012887

Cited by 13 publications

(7 citation statements)

References 35 publications

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“…Intelligent sports to sports equipment and fitness equipment are the basis of human-computer interaction technology, Internet of things, cloud computing, big data, other high-tech implantations, the equipment data, network, intelligent, and entertainment. Different from the current traditional fitness and other sports, intelligent sports can break the limitations of time and space [10]. With the fragmented time and space to use, people can have a more intelligent and convenient way of scientific exercise.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Using Wireless Sensor Network to Correct Posture in Sports Training Based on Hidden Markov Matching Algorithm

Cui

2021

Journal of Sensors

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This paper combines the research of wireless sensor networks and sports training and proposes a wireless sensor network-based intelligent sports training system. According to the requirements of the system, this design uses the wireless sensor network system as the platform for development and the ZigBee module for wireless communication. The advantage of this system is to transmit the obtained information to the ZigBee coordinator module, and after the processing of information and the resultant decision, a nonwearable unmonitored motion training model based on visual sensing is proposed. The motion terminal collects video data streams of user motion actions and extracts features to establish HMM motion recognition algorithm to achieve recognition of motion actions, automatic counting, and intelligent scoring functions. The template matching algorithm based on dynamic time regularization and weighted Euclidean distance realizes a universal real-time motion recognition algorithm with high standard and low latency and can guide the user’s motion action based on similarity calculation. The intelligent sports training system is designed and developed to maintain a high-quality human-computer interaction experience with a real-time feedback client and uploads sports data to a cloud server via the HTTP protocol, which supports real-time sports proximity query and training plan development on the website. After practical application tests, the intelligent sports training system based on the wireless sensor network proposed in this paper is stable and reliable and adds fun and competitiveness to boring sports. The research of this paper has some reference value for the application of wireless sensor networks and the research of the motion recognition algorithm.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Using Wireless Sensor Network to Correct Posture in Sports Training Based on Hidden Markov Matching Algorithm

Cui

2021

Journal of Sensors

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“…Pan T Y et al developed a gesture discrimination way based on multiple inertial measurement unit transducers, which could record the rotation information and acceleration of hand joints. And the average precision in the discrimination of basketball referee signals was 90.02% [9].…”

Section: Related Workmentioning

confidence: 97%

Deep Learning Algorithm based Wearable Device for Basketball Stance Recognition in Basketball

Jiang¹,

Zhang²

2023

IJACSA

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With the continuous improvement of technology, modern sports training is gradually developing towards precision and efficiency, which requires more accurate identification of athletes' sports stances. The study first establishes a classification structure of basketball stance, then designs a hardware module to collect different stance data by using inertial sensors, thus extracting multidimensional motion stance features. Then the traditional convolutional neural network (CNN) is improved by principal component analysis (PCA) to form the PCA+CNN algorithm. Finally, the algorithm is simulated and tested. The outcomes demonstrated that the average discrimination error rate of the improved PCA+CNN algorithm in the Human 3.6M dataset was 3.15%, which was a low error rate. In recognition of basketball sports pose, the wearable based on the improved algorithm had the highest accuracy of 99.4% and took the quietest time of 18s, which was better than the other three methods. It demonstrated that the method had high discrimination precision and recognition efficiency, which could provide a reliable technical means to improve the science of basketball sports training plan and training effect.

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“…The non-invasive sEMG signal can be used to analyze the active state of the muscles and neural activities and performs well in artificial control, clinical diagnosis, motion detection, and neurological rehabilitation [ 54 ]. Hand gesture recognition can be implemented by using a vision camera or wearable sensors [ 108 ]. Wearable sensors and gesture recognition techniques have been used to develop wearable motion sensors for the hearing- and speech-impaired and wearable gesture-based gadgets for interaction with mobile devices [ 109 ].…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Šumak

Brdnik

Pušnik

2021

Sensors

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To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

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Multisensor-Based 3D Gesture Recognition for a Decision-Making Training System

Cited by 13 publications

References 35 publications

[Retracted] Using Wireless Sensor Network to Correct Posture in Sports Training Based on Hidden Markov Matching Algorithm

[Retracted] Using Wireless Sensor Network to Correct Posture in Sports Training Based on Hidden Markov Matching Algorithm

Deep Learning Algorithm based Wearable Device for Basketball Stance Recognition in Basketball

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

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